Nicole Seiberlich

Controlled Aliasing in Volumetric Parallel Imaging (2D CAIPIRINHA)

The CAIPIRINHA (Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration) concept in parallel imaging has recently been introduced, which modifies the appearance of aliasing artifacts during data acquisition in order to improve the subsequent parallel imaging reconstruction procedure. This concept has been successfully applied to simultaneous multi‐slice imaging (MS CAIPIRINHA). In this work, we demonstrate that the concept of CAIPIRINHA can also be transferred to 3D imaging, where data reduction can be performed in two spatial dimensions simultaneously. In MS CAIPIRINHA, aliasing is controlled by providing individual slices with different phase cycles by means of alternating multi‐band radio frequency (RF) pulses. In contrast to MS CAIPIRINHA, 2D CAIPIRINHA does not require special RF pulses. Instead, aliasing in 2D parallel imaging can be controlled by modifying the phase encoding sampling strategy. This is done by shifting sampling positions from their normal positions in the undersampled 2D phase encoding scheme. Using this modified sampling strategy, coil sensitivity variations can be exploited more efficiently in multiple dimensions, resulting in a more robust parallel imaging reconstruction. Magn Reson Med, 2006.

Non-Cartesian Data Reconstruction Using GRAPPA Operator Gridding (GROG)

A novel approach that uses the concepts of parallel imaging to grid data sampled along a non‐Cartesian trajectory using GRAPPA operator gridding (GROG) is described. GROG shifts any acquired data point to its nearest Cartesian location, thereby converting non‐Cartesian to Cartesian data. Unlike other parallel imaging methods, GROG synthesizes the net weight for a shift in any direction from a single basis set of weights along the logical k‐space directions. Given the vastly reduced size of the basis set, GROG calibration and reconstruction requires fewer operations and less calibration data than other parallel imaging methods for gridding. Instead of calculating and applying a density compensation function (DCF), GROG requires only local averaging, as the reconstructed points fall upon the Cartesian grid. Simulations are performed to demonstrate that the root mean square error (RMSE) values of images gridded with GROG are similar to those for images gridded using the gold‐standard convolution gridding. Finally, GROG is compared to the convolution gridding technique using data sampled along radial, spiral, rosette, and BLADE (a.k.a. periodically rotated overlapping parallel lines with enhanced reconstruction [PROPELLER]) trajectories. Magn Reson Med, 2007.

2D‐GRAPPA‐operator for faster 3D parallel MRI

When using parallel MRI (pMRI) methods in combination with three‐dimensional (3D) imaging, it is beneficial to subsample the k‐space along both phase‐encoding directions because one can then take advantage of coil sensitivity variations along two spatial dimensions. This results in an improved reconstruction quality and therefore allows greater scan time reductions as compared to subsampling along one dimension. In this work we present a new approach based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique that allows Fourier‐domain reconstructions of data sets that are subsampled along two dimensions. The method works by splitting the 2D reconstruction process into two separate 1D reconstructions. This approach is compared with an extension of the conventional GRAPPA method that directly regenerates missing data points of a 2D subsampled k‐space by performing a linear combination of acquired data points. In this paper we describe the theoretical background and present computer simulations and in vivo experiments. Magn Reson Med, 2006.

Accelerated volumetric MRI with a SENSE/GRAPPA combination

To combine the specific advantages of the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique and sensitivity encoding (SENSE) with two‐dimensional (2D) undersampling.

Reconstruction of undersampled non-Cartesian data sets using pseudo-Cartesian GRAPPA in conjunction with GROG

Most k‐space‐based parallel imaging reconstruction techniques, such as Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), necessitate the acquisition of regularly sampled Cartesian k‐space data to reconstruct a nonaliased image efficiently. However, non‐Cartesian sampling schemes offer some inherent advantages to the user due to their better coverage of the center of k‐space and faster acquisition times. On the other hand, these sampling schemes have the disadvantage that the points acquired generally do not lie on a grid and have complex k‐space sampling patterns. Thus, the extension of Cartesian GRAPPA to non‐Cartesian sequences is nontrivial. This study introduces a simple, novel method for performing Cartesian GRAPPA reconstructions on undersampled non‐Cartesian k‐space data gridded using GROG (GRAPPA Operator Gridding) to arrive at a nonaliased image. Because the undersampled non‐Cartesian data cannot be reconstructed using a single GRAPPA kernel, several Cartesian patterns are selected for the reconstruction. This flexibility in terms of both the appearance and number of patterns allows this pseudo‐Cartesian GRAPPA to be used with undersampled data sets acquired with any non‐Cartesian trajectory. The successful implementation of the reconstruction algorithm using several different trajectories, including radial, rosette, spiral, one‐dimensional non‐Cartesian, and zig–zag trajectories, is demonstrated. Magn Reson Med 59:1127–1137, 2008.

Self-calibrating GRAPPA operator gridding for radial and spiral trajectories.

Self‐calibrating GRAPPA operator gridding (GROG) is a method by which non‐Cartesian MRI data can be gridded using spatial information from a multichannel coil array without the need for an additional calibration dataset. Using self‐calibrating GROG, the non‐Cartesian datapoints are shifted to nearby k‐space locations using parallel imaging weight sets determined from the datapoints themselves. GROG employs the GRAPPA Operator, a special formulation of the general reconstruction method GRAPPA, to perform these shifts. Although GROG can be used to grid undersampled datasets, it is important to note that this method uses parallel imaging only for gridding, and not to reconstruct artifact‐free images from undersampled data. The innovation introduced here, namely, self‐calibrating GROG, allows the shift operators to be calculated directly out of the non‐Cartesian data themselves. This eliminates the need for an additional calibration dataset, which reduces the imaging time and also makes the GROG reconstruction more robust by removing possible inconsistencies between the calibration and non‐Cartesian datasets. Simulated and in vivo examples of radial and spiral datasets gridded using self‐calibrating GROG are compared to images gridded using the standard method of convolution gridding. Magn Reson Med 59:930–935, 2008.

Zigzag sampling for improved parallel imaging.

Conventional Cartesian parallel MRI methods are limited to the sensitivity variations provided by the underlying receiver coil array in the dimension in which the data reduction is carried out, namely, the phase‐encoding directions. However, in this work an acquisition strategy is presented that takes advantage of sensitivity variations in the readout direction, thus improving the parallel imaging reconstruction process. This is achieved by employing rapidly oscillating phase‐encoding gradients during the actual readout. The benefit of this approach is demonstrated in vivo using various zigzag‐shaped gradient trajectory designs. It is shown that zigzag type sampling, in analogy to CAIPIRINHA, modifies the appearance of aliasing in 2D and 3D imaging, thereby utilizing additional sensitivity variations in the readout direction directly resulting in improved parallel imaging reconstruction performance. Magn Reson Med 60:474–478, 2008.